from sklearn_benchmarks.reporting.hp_match import HPMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HPMatchReporting("sklearnex", config="config.yml")
reporting.run()

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=brute.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.013	0.0	NaN	6.371	0.0	-1	1	NaN	NaN	0.061	0.000	0.205	0.205	See	See
5	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.644	0.0	-1	5	NaN	1.000	0.009	0.000	1.282	1.282	See	See
10	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.415	0.0	1	100	NaN	0.875	0.212	0.010	0.059	0.059	See	See
15	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.544	0.0	-1	100	NaN	NaN	0.049	0.000	0.251	0.251	See	See
20	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.601	0.0	1	5	NaN	1.000	0.001	0.000	16.435	16.785	See	See
25	KNeighborsClassifier_brute_force	False	fit	100000	100000	100	0.012	0.0	NaN	6.598	0.0	1	1	NaN	0.975	0.062	0.001	0.197	0.197	See	See
30	KNeighborsClassifier_brute_force	False	fit	100000	100000	2	0.005	0.0	NaN	0.348	0.0	-1	1	NaN	NaN	0.009	0.000	0.519	0.519	See	See
35	KNeighborsClassifier_brute_force	False	fit	100000	100000	2	0.005	0.0	NaN	0.332	0.0	-1	5	NaN	1.000	0.001	0.000	6.900	7.107	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	17.058	0.101	NaN	0.000	0.017	-1	1	0.663	0.687	0.173	0.004	98.819	98.849	See	See
2	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	1.909	0.033	NaN	0.000	0.002	-1	1	0.663	1.000	0.009	0.000	207.022	207.062	See	See
3	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.303	0.007	NaN	0.000	0.303	-1	1	1.000	NaN	0.048	0.000	6.262	6.262	See	See
4	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.026	0.003	NaN	0.000	0.026	-1	1	1.000	0.742	0.173	0.001	0.148	0.148	See	See
6	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	17.040	0.097	NaN	0.000	0.017	-1	5	0.757	NaN	0.049	0.000	350.556	350.556	See	See
7	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.811	0.027	NaN	0.000	0.003	-1	5	0.757	0.875	0.208	0.001	13.532	13.532	See	See
8	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.305	0.008	NaN	0.000	0.305	-1	5	1.000	0.000	0.009	0.000	32.364	32.368	See	See
9	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.025	0.003	NaN	0.000	0.025	-1	5	1.000	NaN	0.049	0.000	0.524	0.524	See	See
11	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	16.977	0.062	NaN	0.000	0.017	1	100	0.882	0.000	0.009	0.000	1816.460	1816.676	See	See
12	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.067	0.012	NaN	0.000	0.002	1	100	0.882	NaN	0.049	0.000	42.462	42.462	See	See
13	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.304	0.009	NaN	0.000	0.304	1	100	1.000	0.742	0.175	0.001	1.736	1.736	See	See
14	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.025	0.006	NaN	0.000	0.025	1	100	1.000	1.000	0.009	0.000	2.666	2.667	See	See
16	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	17.451	0.158	NaN	0.000	0.017	-1	100	0.882	0.687	0.174	0.001	100.195	100.196	See	See
17	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.866	0.035	NaN	0.000	0.003	-1	100	0.882	1.000	0.009	0.000	304.814	304.991	See	See
18	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.308	0.004	NaN	0.000	0.308	-1	100	1.000	NaN	0.009	0.000	34.993	34.993	See	See
19	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.026	0.002	NaN	0.000	0.026	-1	100	1.000	0.967	0.027	0.001	0.984	0.984	See	See
21	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	16.827	0.055	NaN	0.000	0.017	1	5	0.757	NaN	0.009	0.000	1917.486	1917.486	See	See
22	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	2.049	0.006	NaN	0.000	0.002	1	5	0.757	0.974	0.028	0.000	73.267	73.274	See	See
23	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.308	0.006	NaN	0.000	0.308	1	5	1.000	1.000	0.001	0.000	414.091	420.862	See	See
24	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.022	0.000	NaN	0.000	0.022	1	5	1.000	NaN	0.009	0.000	2.508	2.508	See	See
26	KNeighborsClassifier_brute_force	True	predict	100000	1000	100	16.959	0.024	NaN	0.000	0.017	1	1	0.663	1.000	0.001	0.000	20991.325	21326.580	See	See
27	KNeighborsClassifier_brute_force	False	predict	100000	1000	100	1.185	0.004	NaN	0.001	0.001	1	1	0.663	NaN	0.009	0.000	135.438	135.438	See	See
28	KNeighborsClassifier_brute_force	True	predict	100000	1	100	0.307	0.006	NaN	0.000	0.307	1	1	1.000	0.975	0.060	0.000	5.085	5.085	See	See
29	KNeighborsClassifier_brute_force	False	predict	100000	1	100	0.022	0.002	NaN	0.000	0.022	1	1	1.000	1.000	0.001	0.000	27.403	27.777	See	See
31	KNeighborsClassifier_brute_force	True	predict	100000	1000	2	3.711	0.015	NaN	0.000	0.004	-1	1	0.896	0.974	0.027	0.000	136.426	136.437	See	See
32	KNeighborsClassifier_brute_force	False	predict	100000	1000	2	1.776	0.020	NaN	0.000	0.002	-1	1	0.896	1.000	0.001	0.001	1720.520	1916.230	See	See
33	KNeighborsClassifier_brute_force	True	predict	100000	1	2	0.242	0.005	NaN	0.000	0.242	-1	1	1.000	NaN	0.009	0.000	27.257	27.257	See	See
34	KNeighborsClassifier_brute_force	False	predict	100000	1	2	0.006	0.004	NaN	0.000	0.006	-1	1	1.000	0.967	0.026	0.000	0.231	0.231	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=kd_tree.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.300	0.0	NaN	0.024	0.0	-1	1	NaN	NaN	0.876	0.000	3.766	3.766	See	See
5	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	4.099	0.0	NaN	0.020	0.0	-1	5	NaN	1.000	0.000	0.000	9453.824	10444.309	See	See
10	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.946	0.0	NaN	0.020	0.0	1	100	NaN	0.940	0.656	0.006	6.014	6.015	See	See
15	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.925	0.0	NaN	0.020	0.0	-1	100	NaN	NaN	0.725	0.000	5.414	5.414	See	See
20	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.967	0.0	NaN	0.020	0.0	1	5	NaN	1.000	0.000	0.000	36441.260	47341.929	See	See
25	KNeighborsClassifier_kd_tree	False	fit	1000000	1000000	10	3.969	0.0	NaN	0.020	0.0	1	1	NaN	0.917	0.005	0.000	837.372	840.293	See	See
30	KNeighborsClassifier_kd_tree	False	fit	1000	1000	2	0.003	0.0	NaN	0.005	0.0	-1	1	NaN	NaN	0.004	0.000	0.803	0.803	See	See
35	KNeighborsClassifier_kd_tree	False	fit	1000	1000	2	0.001	0.0	NaN	0.017	0.0	-1	5	NaN	1.000	0.000	0.000	8.117	10.429	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	120.845	0.000	NaN	0.000	0.121	-1	1	0.929	0.910	0.123	0.001	981.581	981.593	See	See
2	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.469	0.004	NaN	0.000	0.000	-1	1	0.929	1.000	0.000	0.000	1658.675	1856.844	See	See
3	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.615	0.144	NaN	0.000	2.615	-1	1	1.000	NaN	0.779	0.000	3.356	3.356	See	See
4	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	-1	1	1.000	0.941	0.222	0.001	0.013	0.013	See	See
6	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	122.155	0.000	NaN	0.000	0.122	-1	5	0.946	NaN	0.753	0.000	162.168	162.168	See	See
7	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.887	0.004	NaN	0.000	0.001	-1	5	0.946	0.940	0.648	0.003	1.369	1.369	See	See
8	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.581	0.109	NaN	0.000	2.581	-1	5	1.000	1.000	0.001	0.000	2890.917	3074.270	See	See
9	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	-1	5	1.000	NaN	0.749	0.000	0.004	0.004	See	See
11	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	130.098	0.000	NaN	0.000	0.130	1	100	0.951	1.000	0.001	0.000	159060.607	171629.162	See	See
12	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	5.374	0.008	NaN	0.000	0.005	1	100	0.951	NaN	0.738	0.000	7.279	7.279	See	See
13	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.608	0.163	NaN	0.000	2.608	1	100	1.000	0.941	0.223	0.002	11.703	11.703	See	See
14	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.003	0.001	NaN	0.000	0.003	1	100	1.000	1.000	0.000	0.000	7.842	8.518	See	See
16	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	133.856	0.000	NaN	0.000	0.134	-1	100	0.951	0.910	0.123	0.002	1090.323	1090.462	See	See
17	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	2.953	0.030	NaN	0.000	0.003	-1	100	0.951	1.000	0.000	0.000	10252.180	11388.749	See	See
18	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.574	0.061	NaN	0.000	2.574	-1	100	1.000	NaN	0.001	0.000	2614.699	2614.699	See	See
19	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.006	0.001	NaN	0.000	0.006	-1	100	1.000	0.879	0.000	0.000	12.289	12.808	See	See
21	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	135.558	0.000	NaN	0.000	0.136	1	5	0.946	NaN	0.002	0.000	67522.040	67522.040	See	See
22	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	1.620	0.004	NaN	0.000	0.002	1	5	0.946	0.905	0.001	0.000	2189.471	2247.740	See	See
23	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.602	0.116	NaN	0.000	2.602	1	5	1.000	1.000	0.000	0.000	23276.934	31109.373	See	See
24	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.002	0.000	NaN	0.000	0.002	1	5	1.000	NaN	0.006	0.000	0.258	0.258	See	See
26	KNeighborsClassifier_kd_tree	True	predict	1000000	1000	10	140.781	0.000	NaN	0.000	0.141	1	1	0.929	1.000	0.000	0.000	1193250.919	1533198.409	See	See
27	KNeighborsClassifier_kd_tree	False	predict	1000000	1000	10	0.848	0.003	NaN	0.000	0.001	1	1	0.929	NaN	0.005	0.000	167.991	167.991	See	See
28	KNeighborsClassifier_kd_tree	True	predict	1000000	1	10	2.585	0.059	NaN	0.000	2.585	1	1	1.000	0.917	0.005	0.000	552.084	553.814	See	See
29	KNeighborsClassifier_kd_tree	False	predict	1000000	1	10	0.001	0.000	NaN	0.000	0.001	1	1	1.000	1.000	0.000	0.000	8.418	10.567	See	See
31	KNeighborsClassifier_kd_tree	True	predict	1000	1000	2	0.037	0.000	NaN	0.000	0.000	-1	1	0.891	0.905	0.001	0.000	48.365	49.280	See	See
32	KNeighborsClassifier_kd_tree	False	predict	1000	1000	2	0.022	0.001	NaN	0.001	0.000	-1	1	0.891	1.000	0.000	0.000	191.472	243.365	See	See
33	KNeighborsClassifier_kd_tree	True	predict	1000	1	2	0.005	0.000	NaN	0.000	0.005	-1	1	1.000	NaN	0.005	0.000	1.067	1.067	See	See
34	KNeighborsClassifier_kd_tree	False	predict	1000	1	2	0.002	0.000	NaN	0.000	0.002	-1	1	1.000	0.879	0.000	0.000	4.943	5.168	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	False	fit	1000000	1000000	2	0.777	0.0	30	0.021	0.0	random	NaN	30	NaN	0.410	0.0	1.898	1.898	See	See
3	KMeans_tall	False	fit	1000000	1000000	2	0.572	0.0	30	0.028	0.0	k-means++	NaN	30	NaN	0.443	0.0	1.291	1.291	See	See
6	KMeans_tall	False	fit	1000000	1000000	100	6.000	0.0	30	0.133	0.0	random	NaN	30	NaN	2.779	0.0	2.159	2.159	See	See
9	KMeans_tall	False	fit	1000000	1000000	100	5.844	0.0	30	0.137	0.0	k-means++	NaN	30	NaN	2.916	0.0	2.004	2.004	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	False	predict	1000000	1000	2	0.001	0.0	30	0.012	0.000	random	0.000	30	0.001	0.0	0.0	9.340	11.378	See	See
2	KMeans_tall	False	predict	1000000	1	2	0.001	0.0	30	0.000	0.001	random	1.000	30	1.000	0.0	0.0	11.353	14.150	See	See
4	KMeans_tall	False	predict	1000000	1000	2	0.001	0.0	30	0.011	0.000	k-means++	0.001	30	0.001	0.0	0.0	8.778	10.391	See	See
5	KMeans_tall	False	predict	1000000	1	2	0.001	0.0	30	0.000	0.001	k-means++	1.000	30	1.000	0.0	0.0	10.938	13.761	See	See
7	KMeans_tall	False	predict	1000000	1000	100	0.002	0.0	30	0.493	0.000	random	0.002	30	0.002	0.0	0.0	5.668	6.279	See	See
8	KMeans_tall	False	predict	1000000	1	100	0.001	0.0	30	0.001	0.001	random	1.000	30	1.000	0.0	0.0	10.078	11.906	See	See
10	KMeans_tall	False	predict	1000000	1000	100	0.002	0.0	30	0.475	0.000	k-means++	0.002	30	0.002	0.0	0.0	5.611	6.207	See	See
11	KMeans_tall	False	predict	1000000	1	100	0.001	0.0	30	0.001	0.001	k-means++	1.000	30	1.000	0.0	0.0	9.310	11.459	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	False	fit	10000	10000	2	0.226	0.0	20	0.001	0.0	random	NaN	20	NaN	0.054	0.0	4.173	4.173	See	See
3	KMeans_short	False	fit	10000	10000	2	0.210	0.0	20	0.001	0.0	k-means++	NaN	20	NaN	0.082	0.0	2.557	2.557	See	See
6	KMeans_short	False	fit	10000	10000	100	0.198	0.0	20	0.040	0.0	random	NaN	20	NaN	0.114	0.0	1.732	1.732	See	See
9	KMeans_short	False	fit	10000	10000	100	0.564	0.0	20	0.014	0.0	k-means++	NaN	20	NaN	0.314	0.0	1.794	1.794	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter_sklearn	iteration_throughput	latency	init	adjusted_rand_score_sklearn	n_iter_sklearnex	adjusted_rand_score_sklearnex	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	False	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	random	0.002	20	0.001	0.001	0.001	1.913	3.118	See	See
2	KMeans_short	False	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	1.000	20	1.000	0.000	0.000	9.655	11.571	See	See
4	KMeans_short	False	predict	10000	1000	2	0.002	0.0	20	0.009	0.000	k-means++	0.002	20	0.001	0.001	0.000	3.354	3.470	See	See
5	KMeans_short	False	predict	10000	1	2	0.001	0.0	20	0.000	0.001	k-means++	1.000	20	1.000	0.000	0.000	10.167	12.325	See	See
7	KMeans_short	False	predict	10000	1000	100	0.002	0.0	20	0.323	0.000	random	0.321	20	0.311	0.001	0.000	2.384	2.450	See	See
8	KMeans_short	False	predict	10000	1	100	0.001	0.0	20	0.001	0.001	random	1.000	20	1.000	0.000	0.000	8.583	9.776	See	See
10	KMeans_short	False	predict	10000	1000	100	0.003	0.0	20	0.307	0.000	k-means++	0.381	20	0.286	0.001	0.000	2.621	2.658	See	See
11	KMeans_short	False	predict	10000	1	100	0.001	0.0	20	0.001	0.001	k-means++	1.000	20	1.000	0.000	0.000	7.364	8.355	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	False	fit	1000000	1000000	100	11.848	0.0	[20]	0.068	0.000	NaN	NaN	NaN	NaN	NaN	2.039	0.0	5.811	5.811	See	See
3	LogisticRegression	False	fit	1000	1000	10000	3.599	0.0	[26]	0.022	0.004	NaN	NaN	NaN	NaN	NaN	0.898	0.0	4.007	4.007	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	class_weight	l1_ratio	n_jobs	random_state	accuracy_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	False	predict	1000000	1000	100	0.000	0.0	[20]	2.881	0.0	NaN	NaN	NaN	NaN	0.56	0.000	0.0	0.799	0.864	See	See
2	LogisticRegression	False	predict	1000000	1	100	0.000	0.0	[20]	0.012	0.0	NaN	NaN	NaN	NaN	1.00	0.000	0.0	0.371	0.440	See	See
4	LogisticRegression	False	predict	1000	100	10000	0.002	0.0	[26]	5.008	0.0	NaN	NaN	NaN	NaN	0.35	0.003	0.0	0.534	0.539	See	See
5	LogisticRegression	False	predict	1000	1	10000	0.000	0.0	[26]	0.882	0.0	NaN	NaN	NaN	NaN	0.00	0.001	0.0	0.131	0.135	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

All estimators share the following hyperparameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	False	fit	1000	1000	10000	0.188	0.0	NaN	0.426	0.0	NaN	NaN	NaN	0.186	0.0	1.012	1.012	See	See
3	Ridge	False	fit	1000000	1000000	100	1.431	0.0	NaN	0.559	0.0	NaN	NaN	NaN	1.125	0.0	1.272	1.272	See	See

	estimator	is_onnx	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	max_iter	random_state	r2_score	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	False	predict	1000	1000	10000	0.012	0.0	NaN	6.588	0.0	NaN	NaN	0.083	0.02	0.0	0.616	0.616	See	See
2	Ridge	False	predict	1000	1	10000	0.000	0.0	NaN	1.155	0.0	NaN	NaN	NaN	0.00	0.0	0.625	0.739	See	See
4	Ridge	False	predict	1000000	1000	100	0.000	0.0	NaN	5.271	0.0	NaN	NaN	1.000	0.00	0.0	0.617	0.699	See	See
5	Ridge	False	predict	1000000	1	100	0.000	0.0	NaN	0.013	0.0	NaN	NaN	NaN	0.00	0.0	0.645	0.803	See	See